Network-based gene function prediction in mouse and other model vertebrates using mousenet server

Eiru Kim, Insuk Lee

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)


The mouse, Mus musculus, is a popular model organism for the study of human genes involved in development, immunology, and disease phenotypes. Despite recent revolutions in gene-knockout technologies in mouse, identification of candidate genes for functions of interest can further accelerate the discovery of novel gene functions. The collaborative nature of genetic functions allows for the inference of gene functions based on the principle of guilt-by-association. Genome-scale co-functional networks could therefore provide functional predictions for genes via network analysis. We recently constructed such a network for mouse (MouseNet), which interconnects over 88% of protein-coding genes with 788,080 functional relationships. The companion web server ( enables researchers with no bioinformatics expertise to generate predictions that facilitate discovery of novel gene functions. In this chapter, we present the theoretical framework for MouseNet, as well as step-by-step instructions and technical tips for functional prediction of genes and pathways in mouse and other model vertebrates.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Number of pages16
Publication statusPublished - 2017

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745

Bibliographical note

Funding Information:
This work was supported by grants from the National Research Foundation of Korea (2015R1A2A1A15055859, 2012M3A9B4028641, 2012M3A9C7050151).

Publisher Copyright:
© Springer Science+Business Media LLC 2017.

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics


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